It's IMHO amazingly impressive how dense information can be stored within neural networks. Even a comparably tiny LLM can store more information than the human brain, despite the brain's theoretical storage being far higher due to its vast number of connections (ANNs are better at information density, we're better at learning from limited datasets). The tiny LLM will crush humans at a quiz in virtually anything except said human's particular areas of expertise. Storing information as a superposition of states across a large number of neurons and connections (whether we're talking artificial or biological) is an immensely space-efficient way to do so, and the human mind is nowhere near the limits of information storage capability.

There is no technical reason why a given organism, such as a bee, could not achieve far denser information representations in order to be able to do more with its limited neural capacity (though there are always tradeoffs). One of the reasons that ANNs learn slower-but-denser is the use of a very low learning rate with a very large amount of data that covers the same topic from many different angles, giving the weights ample time to explore different possible circuits in parallel and seeing which ones predict reality the best ("learn everything all at once" vs. "learn this thing NOW"). Bees aren't tasked with learning anywhere nearly as diverse things as a human is and spend all day doing the same basic job (the same information "from different angles"), so it seems quite possible that their greater "information specialization" as they go about their day may be able to lead to denser representations of said information.

BTW, at risk of a tangent (your comment about non-neuron cells playing roles), it's been really interesting to me seeing how a key difference between artificial and biological learning has been clearing up. In biological neural networks, weight cannot flip sign (Dale's Principle). In the general case, a neuron is either excitatory or inhibitory (usually a small number of inhibitory neurons per cluster of excitatory neurons); it can't change from one to the other even if learning would favour that. At a first glance, that would seem to cripple learning capability (and definitely does if you implement that in ANNs). But what appears to actually happen in biological neural networks is a sort of horizontal learning, co-dependent synaptic plasticity, between excitatory and inhibitory neurons. Instead of merely weakening an excitatory connection down to zero and then being able to go no further, learning simultaneously weakens the excitatory connections and strengthens the inhibitory connections. The excitatory neurons are the primary drivers of information storage and processing, but the inhibitory neurons adjust the baseline to give them the flexibility to express negative net activations as needed.

It is a myth that individual bees only retain information for half an hour. Depending on the memory at hand, bee memories can last days, weeks, or even the remainder of their foraging life. They have to remember things, because the timeframes a hive operates on are much longer than half an hour, including night time and being kept inside by inclement weather for days or even weeks at at time. Individual bees also learn much more than can be conveyed through waggle dances, such as what colours and shapes of flowers are yielding best in a given area at what time of day (bee learning is essential to them being able to function as generalists, able to handle any mix of plants at any latitude).

Also, the hive doesn't just blindly accept whatever any bee says. Each bee functions as an individual in a society. When a bee waggles in the "town square" (on the comb), other bees gather around to "listen" (detecting oscillating shifts in the electric field plus tactile contact and sound). But whether a bee actually decides to make use of that information depends on whether they're having good or bad foraging success. Only a small fraction of bees on average (usually a single-digit percentage of watchers) will decide to make use of the information. And if another bee "disagrees" with a waggle dance - for example, if they've been there and found nothing, or worse, found dead bees, predators or a rival hive), they can make a counter-buzz to argue against it. The arguments can get quite "heated", with many bees taking part.

We think of bees as mindless drones (literally, we took the very word!), but they're all individuals each acting on their own. There are simply various rallying factors that keep them together (for example, the scent of the queen, the desire to live in a warm hive, etc). The information communicated within a hive is limited; bees overwhelmingly rely on their own mind and memory, and perform their tasks as individuals.

I mean, bees attack small hive beetles by building prisons of propolis around them, that's something :)

Meanwhile, X.AI has asked the EPA for permission to release up to 100 million hyperfecund pesticide-resistant male mosquitoes in California and New York over two years to own the libs.

*head bangs in approval*

Oh, forgot to link the dry density for you: here you go. 341kcal/100g. Aka 3,41kcal/g.

Which, like I said, should be obvious, since they're almost entirely carbs (~4kcal/g) and protein (~4kcal/g), and they're, as noted, dry (12-16% moisture). It would be quite the trick indeed to get something that is dry and and is almost entirely comprised of things that are 4kcal/g to be 1,38kcal/g! ;)

Just in case you need help:

Your calculation: 195g (dry weight) × 1.38 kcal/g = 269 calories per pound of cooked beans.
Correction: Because you used 1.38 kcal/g (the cooked density) as if it were the dry density, you essentially diluted the calories twice.
The Actual Math: 195g of dry beans * 3.4 kcal/g (actual dry density) = 663 kcal.

When those 195g of dry beans absorb water to weigh 454g (1 pound), they still contain those same 663 calories (since water has zero calories).

Canned beans are ALREADY COOKED. *facepalm*. You can eat them straight out of the can.

which is waaaay more than I would want to eat at a sitting.

I can't think of a single ingredient - any ingredient - that I would want to eat exclusively as my diet, so this is a really stupid argument.

In general, "damping pleasure" is not most people's experience with GLP-1 agonists. What it does is more like separate pleasurable experiences from having an urgent need to continue doing them.

I'd believe the Iceland numbers. I had a doctor once who wanted to get me on antidepressants, and got mad when I didn't want to, and completely ignored my pleadings of "But I'm not depressed", "I enjoy life", "I'm probably the least depressed person you'll meet", etc. He just really liked his patients to be on it. The Icelandic medical system is very into anything that "medicates symptoms" rather than treating diseases. For example, during COVID, it was essentially impossible to get drugs like paxlovid, but they made parkodín (tylenol with codeine) over-the-counter.

In most modern societies medication is usually a last resort.

I'm going to take a wager that if I were to open your medicine cabinet right now, there would be painkillers in it, which you take as will when you get headaches, body aches, etc.

Yes, different people have different baseline hunger levels. This is well accepted in the scientific community.

Please read: Cooked bean variety.

The "beans in your pantry" data you're looking at are probably per serving. Here, let me grab the beans in *my* pantry. Roland BLACK BEANS Habichuelas Negras Supreme Calidad. Net weight 15.5 OZ / 439g. Serving size: 130g. Calories per serving: 180.

There's 453,6 grams per pound, so that's 0,968 pounds. 439/130 = 3,15 servings, times 180 calories = 567. In 0,968 pounds, that's 586 calories.

Or look online. "172 grams of black beans (cooked, boiled, unsalted) contains 227 Calories." Do the math.

I'm not sure exactly how you expect something that is 70% carbs (of which are 36% fibre) and 26% protein to be low-cal. Do you think it has the moisture content of celery or something?

I'm thinking about starting a very low dose when the pills come out in Europe. That gives an extra year for more data.

For me it's purely about health (well, about 90% about healthj). I'm a marginal case weight-wise, but the overall health impact profile looks spectacular. If a pill seems likely to add a number of healthy years to my lifespan, yes please. But the more data the better.

One thing that held me back was, I'm very averse to addiction, to anything that might have withdrawal symptoms. People report being ravenous and needing to eat all the time when they quit. BUT - the data shows that after one year, people still retain about 25% of their weight loss, and at two years they're about baseline (some above baseline, some below - the "above" people may be due to sarcopenic obesity, in that you put fat back on faster than muscle, and so your metabolism is lower until the muscle comes back). This is very different from when you diet to lose weight and then stop dieting - you're not ravenous at all, you finally have satiation.

But given the weight regain stats, and the general way these work, what I think is going on is: when you lose weight, you've been training yourself for months on how to ignore or alleve your hunger pangs, so when you stop, you're well trained to it. Whereas GLP-1 agonists are just the opposite: you don't even need to think about resisting the temptation to eat, it just comes naturally; you can get pleasure from something, such as a tasty dessert, without feeling the need to eat everything on your plate; pleasure and craving get separated. So people who just suddenly cut off from GLP-1 agonists are "mentally unarmed" for the reversal. The weight-regain stats however suggest that it doesn't leave you long-term disabled in this regard; that you're just back to your old self once you readjust, whatever that old self may have been.

I haven't read these particular studies, but a lot of the fascinating impacts of GLP-1 agonists occur whether the person loses weight or not. For example, the cardiac benefits are massive, like 2/3rds of the scale of benefits of being on statins, and it apparently occurs independent of weight loss.

One of the annoying things about our wetware is that systems aren't isolated; a "part" that gets used for one thing might also be used for half a dozen unrelated things.

Please understand that there is a balance. Taking things to "reduce inflammation" or to "boost the immune system" run counter to each other. Inflammation *is* the reaction of the innate immune system. The immune system defends not just against pathogens, but also cancer. If you shut down the immune system too much, you can shut down cancer surveillance, which I don't need to stress, is a bad thing.

The downside to inflammation is that, yes, it is damaging. Needless inflammation is bad. And, as an added twist, from a personal example: my mother has Sjögren's and MALT lymphoma in the salivary glands. Sjögren's is an autoimmune condition that attacks exocrine glands. In doing so, it triggers a nonstop immune reaction in the salivary glands and the development of lymphoid tissue, with lymphocytes constantly proliferating. This nonstop proliferation runs the risk of - as in my mother's case - developing mutations that lead to lymphoma. So too much of a needless immune reaction can also cause cancer.

The immune system is an extremely complex, with hundreds of known cytokines, each causing various activation / suppression effects in others and having various other interactions with the body. So it's extremely hard to say, if you tweak this one thing, what will be the overall impact in the long term?

These GLP-1 agonists inhibit the NF-kB pathway and downregulate pro-inflammatory cytokines like TNF-a, IL-6, and IL-1. We think that this sort of downregulation is probably in general beneficial, in that in most cases it should not weaken cancer surveilance, and actually can help with certain types of cancers (but still can be harmful to some). Everything is situation dependent, and there's a lot we don't know.